Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish read-only/open-world/idempotent safety. The description adds behavior: it calls ai_visibility_check per entity, ranks results by score, identifies most/least recognized, and returns score, confidence, and signal density per entity. This informs expected side effects (none) and output structure. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, mechanism (probes/ranks), and use case/return. Every sentence provides unique information; no redundant fillers. Well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only comparison tool with good schema and annotations, the description tells the agent what it does, how (via ai_visibility_check), and what it returns (ranked list with score, confidence, signal density). That's adequate for correct invocation even without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters (entities, models, _apiKey, context). The description does not elaborate on parameter usage beyond what the schema says, though it echoes the 'first entry as subject' concept from the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Compare AI visibility across multiple entities side-by-side' – a specific verb (compare) + resource (AI visibility) + scope (multiple entities). It differentiates from the sibling ai_visibility_check by explicitly mentioning it probes each entity and ranks them, making it the multi-entity comparison variant.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear use case: 'competitive AI-marketing audits: "does Claude know about us as well as our competitors?"'. It implies this is the multi-entity counterpart to ai_visibility_check by stating it probes each entity with that tool. However, it doesn't explicitly say when not to use it or name other alternatives like compare_entities, so I give 4 rather than 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.